1. Introduction to R and RStudio

   • Working with packages
   • Getting help in R

2. Reproducible data analysis with R Markdown

3. Organise your work with R Projects

• R is a language and environment for statistical computing and for graphics
• Based on the object-oriented language S (1975)
• 100% free software
• Managed by the R Foundation for Statistical Computing, Vienna, Austria.
• Community-driven:
  • More than 10.000 packages developed by community
  • New packages are constantly being developed
  • New features are constantly being added to existing packages

Every version of R that is released is named after a topic in a Peanuts comic. The R version 4.3.3 (2024-02-29) is called “Angel Food Cake”.

R version	Charlie Brown cartoon

• RStudio is an Integrated Development Environment (IDE)
• RStudio has all functionality in one place and makes working with R much easier.
• Use RStudio to:
  • Edit scripts, Run scripts
  • Manage your code with highlighting
  • Navigate files, organize projects
  • Utilize version control (e.g. Github)
  • View static and interactive graphics
  • Create different file types (RMarkdown, Shiny apps)
  • Work with different languages (Python, JavaScript, C++, etc.)

• When you start RStudio and R only the base packages are activated: the basic installation with basic functionality.

• There are almost 20.000 packages that have been developed by R users all over the world. See the Comprehensive R Archive Network (CRAN)

• Not efficient to have all these packages installed every time you use R. Install only the packages you want to use.

• Use sessionInfo() to see which packages are active.

• This is how the basic installation looks like:

An overview of the packages you have installed, see the tab “Packages” in the output pane:

Packages are like apps on your mobile phone.

• When you want to use a package for the first time, you have to install the package.

• Each time you want to use the package, you have to load (activate) it.

To load a package use the following code (similar to opening an app on your phone):

library(ggplot2)

To close a package use (similar to closing an app on your phone):

detach(ggplot2)

The need to combine code and text and to document all the steps to make reproducible (scientific) reports of data analyses.

To collaborate effectively and use version control with Github.

Images source

It is efficient. Generate and update reports in all kinds of formats:

Source: What is R Markdown? Video RStudio

See the R Markdown Cheat Sheet for a complete list of options.

Code chunks start with {r } (for R code). You can give code chunks names (here cars).

This is how the result looks like in the rendered html document. Display of both R code and results:

You can choose to hide the R code with echo=FALSE in the chunk header:

See the R Markdown Cheat Sheet for a complete list of knitr chunk options.

R Markdown Cheat Sheet:

Every time you start a new (data analysis) project, make it a habit to create a new RStudio Project.

Because you want your project to work:

• not only now, but also in a few years;
• when the folder and file paths have changed;
• when collaborators want to run your code on their computer.

RStudio Projects create a convention that guarantees that the project can be moved around on your computer or onto other computers and will still “just work”. It creates relative paths (no more broken paths!).

All data, scripts, and output should be stored within the project directory.

Every time you want to work on this project: open the project by clicking the .Rproj file.

• Open the course manual
• Click on the tab “Monday”
• Go to Course material and click on Lecture + Lab 1a, which starts the download of a folder with:
  • A html file of the lecture: “1a RStudio Rmarkdown.html”
  • A html file of the lab: “1a Lab.html”